Sensor data wrangling

Load packages

Read in the data

all <- read_csv(here("data","sensors", "sensor-data_all.csv")) %>% 
  clean_names() %>%
  mutate(date_time=mdy_hms(date_time), #aapply lubridate to date/time column
         date=format(date_time, '%m/%d/%Y'), #create only date column
         time=format(date_time, '%H:%M:%S')) %>% #create only time column
 select(site, sensor_number, date_time, date, time, temp_c, p_h) %>%
    mutate(site=replace(site, site=="LOL", "Lompoc Landing"),
           site=replace(site, site=="ALG", "Alegria"),
           site=replace(site, site=="BML", "Bodega Bay")) #rename locations

## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   Site = col_character(),
##   `Sensor number` = col_double(),
##   `Download date` = col_character(),
##   `Calibration date` = col_character(),
##   `Temp_(C)` = col_double(),
##   `Voltage#1` = col_double(),
##   TK = col_double(),
##   `S(T)` = col_double(),
##   `Eo(T)` = col_double(),
##   pH = col_double(),
##   `Date time` = col_character()
## )

Plot up the pH

#set site order for plotting (legend)
all$site <- factor(all$site, levels=c("Alegria", "Lompoc Landing", "Bodega Bay"))

all_filter <- all %>%
  filter(p_h < 8.5) %>%
  mutate(removeit = ifelse(site=="Alegria" & date_time<ymd_hms("2021-06-30 23:00:00"), "remove", "keep")) %>%
  filter(removeit=="keep")

ggplot(all_filter, aes(x=date_time, y=p_h, group=site)) +
  geom_line(aes(color=site), size=0.7) +
  geom_point(aes(color=site), size=0.5) +
  scale_x_datetime(breaks = scales::date_breaks("1 week"), 
                    labels = date_format("%m/%d %H:%m")) +
  xlab("Date time") +
  ylab("pH") +
  theme_bw() +
  theme(legend.title=element_blank(),
        axis.text.x=element_text(angle=45, vjust = 1, hjust=1, size=12),
        axis.title.x=element_text(size=15),
        axis.text.y=element_text(size=12),
        axis.title.y=element_text(size=15),
        legend.text=element_text(siz=12))

ggsave(here("figures", "sensors", "june-sept.png"), height=20, width=40, units="cm")

Plot temp up for good measure

ggplot(all, aes(x=date_time, y=temp_c, group=site)) +
  geom_line(aes(color=site), size=0.7) +
  geom_point(aes(color=site), size=0.5) +
  scale_x_datetime(breaks = scales::date_breaks("1 week"), 
                    labels = date_format("%m/%d %H:%m")) +
  xlab("Date time") +
  theme_bw() +
  theme(axis.text.x=element_text(angle=90))

ggsave(here("figures", "sensors","june-sept_temp.png"), height=20, width=40, units="cm")

Plot each site, temp and pH

BML

bml <- all %>%
  filter(site=="Bodega Bay")

ggplot(bml, aes(x=date_time)) +
  geom_line(aes(y=p_h), color="red") + 
  geom_line(aes(y=temp_c), color="blue") + # Divide by 10 to get the same range than the temperature
  scale_y_continuous(name = "pH", #first axis name
    sec.axis = sec_axis(~., name="Temp (C)")) + #second axis name and features
  scale_x_datetime(breaks = scales::date_breaks("1 week"), 
                    labels = date_format("%m/%d %H:%m")) +
  xlab("Date time") +
  theme_bw() +
  theme(axis.text.x=element_text(angle=90))

## Try it with pH and temp sorted as "groups"
bml2 <- bml %>%
  pivot_longer(cols=temp_c:p_h,
               names_to = "group",
               values_to = "value")

ggplot(bml2, aes(x=date_time, y=value, group=group)) +
  geom_line(aes(color=group), size=0.7) +
  #geom_point(aes(color=group), size=0.5) +
  scale_x_datetime(breaks = scales::date_breaks("1 week"), 
                    labels = date_format("%m/%d %H:%m")) +
  xlab("Date time") +
  theme_bw() +
  theme(axis.text.x=element_text(angle=90))

Let’s come back to this…

Add tide cycles!

Customize HTML

Sites:

• Bodega (Bodega%20Harbor%20entrance%2C%20California)
• Lompoc (Point%20Arguello%2C%20California)
• Alegria (Gaviota%2C%20California)

Glen: number of days to extract

Interval:

• 10 minutes (00%3A10)
• 15 minutes (00%3A15)
• 1 hour (01%3A00)

BML

Scrape tide data from http://tbone.biol.sc.edu/tide/

bml_tide <- read_html("http://tide.arthroinfo.org/tideshow.cgi?tplotdir=horiz;gx=640;gy=240;caltype=ndp;type=mrare;interval=00%3A15;glen=150;units=feet;year=2021;month=05;day=31;hour=09;min=30;tzone=local;d_year=;d_month=01;d_day=01;d_hour=00;d_min=00;ampm24=24;site=Bodega%20Harbor%20entrance%2C%20California") %>%
  html_elements("pre") %>% #select only the date, time, and tide values from the webpage
  html_text2() %>% #convert list to data table
  data.frame() %>% #convert table to data frame
  mutate(date_tide = str_split(., pattern = "\n")) %>% #split into rows by each time point
  unnest(date_tide) %>% #unnest into two columns 
  mutate(date_tide=as.factor(date_tide)) %>% #make column values factors 
  separate(date_tide, into = c("date", "space", "time", "time_zone", "tide"), sep="\\s") %>% #separate the values (separated by spaces) into their own columns
  select(-"space", -".") %>% #remove the "space" (blank space) column and duplicated column created by unnest() 
  unite("time", "time", "time_zone", sep="\ ",) %>% #join together time and time zone
  unite("date_time", "date", "time", sep="\ ") %>% #join together date and time/time zone
  drop_na() %>% #remove final row with NA (not sure why that's even there)
  mutate(date_time=ymd_hm(date_time), #apply lubridate to date/time column
         tide=as.numeric(tide)) #coerce tide values from character to numeric

Plot it up

ggplot(bml_tide, aes(x=date_time, y=tide)) +
  geom_line(size=0.7) +
  scale_x_datetime(breaks = scales::date_breaks("2 days"), 
                    labels = date_format("%m/%d %H:%m")) +
  xlab("Date time") +
  ylab("Tide height") +
  theme_bw() +
  theme(axis.text.x=element_text(angle=90))

ggsave(here("figures", "sensors", "bml_tide.png"), height=20, width=40, units="cm")

Combine the tides with the pH/temp values for BML, plot it up!

bml_all <- full_join(bml, bml_tide) %>%
  drop_na(c(tide, p_h)) %>% #whoops, started off collecting data every 10 minutes and then switched to 15 minutes (so account for that by removing pH and tide values that don't overlap)
  distinct(date_time, .keep_all = TRUE) #remove duplicated data (not sure where those came from)

## Joining, by = "date_time"

bml3 <- bml_all %>%
  pivot_longer(cols=temp_c:tide,
               names_to = "data",
               values_to = "value")
  
ggplot(bml3, aes(x=date_time, y=value, group=data)) +
  geom_line(aes(color=data), size=0.7) +
  scale_x_datetime(breaks = scales::date_breaks("1 week"), 
                    labels = date_format("%m/%d %H:%m")) +
  xlab("Date time") +
  theme_bw() +
  theme(axis.text.x=element_text(angle=90))

ggsave(here("figures", "sensors", "bml_pH_temp_tide.png"), height=20, width=40, units="cm")

Remove measurements where the sensor was out of the water (at a tide lower than -0.5, possibly higher (TBD))

bml_detide <- bml_all %>%
  filter(tide>-0.5) %>%
  drop_na(c(p_h)) #drop observations that don't overlap (10 min vs 15 min sampling interval)

ggplot(bml_detide, aes(x=date_time)) +
  geom_line(aes(y=tide), color="red") + 
  geom_line(aes(y=temp_c), color="blue") + # Divide by 10 to get the same range than the temperature
  scale_x_datetime(breaks = scales::date_breaks("1 week"), 
                    labels = date_format("%m/%d %H:%m")) +
  xlab("Date time") +
  theme_bw() +
  theme(axis.text.x=element_text(angle=90))

Check the tide height filtering for BML

bml_superfilter <- bml_all %>%
  filter(date_time > ymd_hms("2021-07-19 23:00:00"),
         date_time < ymd_hms("2021-07-26 23:00:00"))  #7-30 to 8-06 is also interesting

#Looking at the mid July dates, looks like I might need to filter out tides below -0.2? But then if I look at other dates, it's not as bad. -0.4 might actually be a good decision.

y1 <- bml_superfilter$p_h
y2 <- bml_superfilter$temp_c
y3 <- bml_superfilter$tide
x <- bml_superfilter$date_time

highchart() %>% 
  hc_add_series(data = y3, dashStyle="solid") %>% 
  hc_add_series(data = y2, yAxis = 1) %>% 
  hc_yAxis_multiples(
     list(lineWidth = 3, lineColor="#009E73", title=list(text="Tide cycle")),
     list(lineWidth = 3, lineColor="#0072B2", title=list(text="Temperature"))) %>%
    hc_xAxis(title = "Date", categories = x, breaks=10) %>%
  hc_colors(c("#009E73","#0072B2"))

Let’s filter more, now based on anomalous temperature measurements (spikes in July)

bml_detide_temp <- bml_detide %>%
  filter(!(date_time >= ymd_hms("2021-07-10 06:00:00") & date_time <= ymd_hms("2021-07-10 07:30:00")),
         !(date_time >= ymd_hms("2021-07-21 06:15:00") & date_time <= ymd_hms("2021-07-21 07:15:00")),
         !(date_time >= ymd_hms("2021-07-22 06:15:00") & date_time <= ymd_hms("2021-07-22 08:00:00")),
         !(date_time >= ymd_hms("2021-07-23 07:15:00") & date_time <= ymd_hms("2021-07-23 08:30:00")))

ggplot(bml_detide_temp, aes(x=date_time)) +
  geom_line(aes(y=temp_c), color="red") + 
  geom_line(aes(y=tide), color="blue") + # Divide by 10 to get the same range than the temperature
  scale_x_datetime(breaks = scales::date_breaks("1 day"), 
                    labels = date_format("%m/%d %H:%m")) +
  xlab("Date time") +
  theme_bw() +
  theme(axis.text.x=element_text(angle=90))

Filter even more, based on pH measurements, and generate final plot

# bml_detide_temp_ph <- bml_detide_temp %>%
#   arrange(date_time) %>% #make sure observations are in order by date/time
#   mutate(diff3 = p_h - lag(p_h, default = first(p_h))) %>% #find difference between two subsequent pH measurements to identify anomalies
#   filter(diff3>-0.5,
#          diff3<0.5) #remove weird readings on 7/10
# 
# ggplot(bml_detide_temp_ph, aes(x=date_time)) +
#   geom_line(aes(y=p_h), color="#009E73") + 
#   geom_line(aes(y=temp_c), color="#D55E00") + # Divide by 10 to get the same range than the temperature
#   geom_line(aes(y=tide), color="#0072B2") + # Divide by 10 to get the same range than the temperature
#   scale_x_datetime(breaks = scales::date_breaks("1 week"), 
#                     labels = date_format("%m/%d %H:%m")) +
#   annotate(geom="text", x=as.POSIXct("2021-05-31 00:10:00"), y=14, hjust=0, label="Temp (C)", color="#D55E00", size=5) +
#   annotate(geom="text", x=as.POSIXct("2021-08-16 00:10:00"), y=9, hjust=-0.1, label="pH", color="#009E73", size=5) +
#   annotate(geom="text", x=as.POSIXct("2021-05-31 00:10:00"), y=6, hjust=0, label="Tide", color="#0072B2", size=5) +
#   xlab("Date & time") +
#   ylab("Value") +
#   theme_bw() +
#   theme(axis.text.x=element_text(angle=45, vjust = 1, hjust=1, size=12),
#         axis.title.x=element_text(size=15),
#         axis.text.y=element_text(size=12),
#         axis.title.y=element_text(size=15))
# 
# ggsave(here("figures", "sensors", "bml_detide_tide-temp-pH.png"), height=20, width=40, units="cm")

LOL

Scrape tide data from http://tbone.biol.sc.edu/tide/

lol_tide <- read_html("http:///tide.arthroinfo.org/tideshow.cgi?tplotdir=horiz;gx=640;gy=240;caltype=ndp;type=mrare;interval=00%3A15;glen=150;units=feet;year=2021;month=06;day=14;hour=08;min=00;tzone=local;d_year=;d_month=01;d_day=01;d_hour=00;d_min=00;ampm24=24;site=Point%20Arguello%2C%20California") %>%
  html_elements("pre") %>% #select only the date, time, and tide values from the webpage
  html_text2() %>% #convert list to data table
  data.frame() %>% #convert table to data frame
  mutate(date_tide = str_split(., pattern = "\n")) %>% #split into rows by each time point
  unnest(date_tide) %>% #unnest into two columns 
  mutate(date_tide=as.factor(date_tide)) %>% #make column values factors 
  separate(date_tide, into = c("date", "space", "time", "time_zone", "tide"), sep="\\s") %>% #separate the values (separated by spaces) into their own columns
  select(-"space", -".") %>% #remove the "space" (blank space) column and duplicated column created by unnest() 
  unite("time", "time", "time_zone", sep="\ ",) %>% #join together time and time zone
  unite("date_time", "date", "time", sep="\ ") %>% #join together date and time/time zone
  drop_na() %>% #remove final row with NA (not sure why that's even there)
  mutate(date_time=ymd_hm(date_time), #apply lubridate to date/time column
         tide=as.numeric(tide)) #coerce tide values from character to numeric

Plot it up

ggplot(lol_tide, aes(x=date_time, y=tide)) +
  geom_line(size=0.7) +
  scale_x_datetime(breaks = scales::date_breaks("2 days"), 
                    labels = date_format("%m/%d %H:%m")) +
  xlab("Date time") +
  ylab("Tide height") +
  theme_bw() +
  theme(axis.text.x=element_text(angle=90))

ggsave(here("figures", "sensors", "lol_tide.png"), height=20, width=40, units="cm")

Extract temp values from HOBO logger, clean up for overlap with LOL readings

lol_temp <- read_csv(here("data","sensors", "hobo_lol.csv")) %>% 
  filter(row_number()!=1) %>% # remove the first row
  clean_names() %>%
  rename(timestamp=x2,
         temp_hobo_f=x3) %>%
  mutate(date_time=ymd_hms(timestamp), #aapply lubridate to date/time column
         date=format(date_time, '%m/%d/%Y'), #create only date column
         time=format(date_time, '%H:%M:%S'), #create only time column
         temp_hobo_c=((as.numeric(temp_hobo_f)-32)*(5/9))) %>% #convert F to C from HOBO temp data
  select(temp_hobo_c,date_time) #select the only columns that really matter

## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   `Plot Title: LOL` = col_character(),
##   X2 = col_character(),
##   X3 = col_character()
## )

Combine the tides with the pH/temp values for LOL, plot it up!

# filter all data for LOL
lol <- all %>%
  filter(site=="Lompoc Landing") %>%
  distinct(date_time, .keep_all = TRUE) #remove duplicated data (not sure where those came from)

#join HOBO data with Durafet data
lol_all_1 <- full_join(lol, lol_temp) %>%
  drop_na(c(temp_c, p_h)) %>% #remove hobo measurements taken outside of Durafet measurements
  rename(temp_durafet_c=temp_c) 

## Joining, by = "date_time"

#Check difference between Durafet and HOBO temperature measurements
lol_temp_diff <- lol_all_1 %>%
  mutate(diff=temp_durafet_c-temp_hobo_c)

#Plot it up to check for anomalies
y1 <- lol_temp_diff$temp_durafet_c
y2 <- lol_temp_diff$temp_hobo_c
y3 <- lol_temp_diff$diff
x <- lol_temp_diff$date_time

highchart() %>% 
  hc_add_series(data = y1, dashStyle="solid") %>% 
  hc_add_series(data = y2, yAxis = 1) %>% 
  hc_add_series(data = y3, yAxis = 2) %>%
  hc_yAxis_multiples(
     list(lineWidth = 3, lineColor='#D55E00', title=list(text="durafet")),
     list(lineWidth = 3, lineColor="#009E73", title=list(text="hobo")),
     list(lineWidth = 3, lineColor="#0072B2", title=list(text="diff"))) %>%
    hc_xAxis(title = "Date", categories = x, breaks=10) %>%
  hc_colors(c("#D55E00","#009E73", "#0072B2"))

#Join pH, temp data with tide data
lol_all <- full_join(lol_all_1, lol_tide) %>%
  drop_na(c(tide, p_h)) %>% #remove unnecessary tide values
  rename(temp_c=temp_hobo_c) 

## Joining, by = "date_time"

#Check if anomalous points in durafet vs hobo are due to tide cycle?
lol_check <- lol_all %>%
  mutate(diff=temp_durafet_c-temp_c) %>%
  filter(tide<3.232) #this value was extracted thanks to code later on
  #filter((date_time > ymd_hms("2021-10-01 01:00:00") & date_time <= ymd_hms("2021-10-22 24:00:00")))

#create a highchart to assess
y1 <- lol_check$temp_durafet_c
y2 <- lol_check$temp_c
y3 <- lol_check$diff
x <- lol_check$date_time

highchart() %>% 
  hc_add_series(data = y1, dashStyle="solid") %>% 
  hc_add_series(data = y3, yAxis = 1) %>% 
  hc_add_series(data = y3, yAxis = 2) %>%
  hc_yAxis_multiples(
     list(lineWidth = 3, lineColor='#D55E00', title=list(text="durafet")),
     list(lineWidth = 3, lineColor="#009E73", title=list(text="hobo")),
     list(lineWidth = 3, lineColor="#0072B2", title=list(text="diff"))) %>%
    hc_xAxis(title = "Date", categories = x, breaks=10) %>%
  hc_colors(c("#D55E00","#009E73", "#0072B2"))

# Anomalous points aside, plot it "all" up
lol_plot <- lol_all %>%
  pivot_longer(cols=temp_durafet_c:tide,
               names_to = "data",
               values_to = "value")
  
ggplot(lol_plot, aes(x=date_time, y=value, group=data)) +
  geom_line(aes(color=data), size=0.7) +
  scale_x_datetime(breaks = scales::date_breaks("1 week"), 
                    labels = date_format("%m/%d %H:%m")) +
  xlab("Date time") +
  theme_bw() +
  theme(axis.text.x=element_text(angle=90))

ggsave(here("figures", "sensors", "lol_pH_temp_tide.png"), height=20, width=40, units="cm")

Remove measurements where the sensor pool was disconnected from the ocean (at a tide lower than +0.5, likely higher (TBD))

lol_detide <- lol_all %>%
  filter(tide>0.5)

ggplot(lol_detide, aes(x=date_time)) +
  geom_line(aes(y=tide), color="red") + 
  geom_line(aes(y=temp_c), color="blue") +
  scale_x_datetime(breaks = scales::date_breaks("1 week"), 
                    labels = date_format("%m/%d %H:%m")) +
  xlab("Date time") +
  theme_bw() +
  theme(axis.text.x=element_text(angle=90))

Check the peaks (the peak is where the tide starts coming back in - when the pool is re-connected to the ocean and the temp drops), and ID where they are so I can filter

lol_check <- lol_detide %>%
  filter(date_time < ymd_hms("2021-06-30 23:00:00")) %>%
  arrange(date_time) %>% #make sure observations are in order by date/time
  mutate(diff = temp_c - lag(temp_c, default = first(temp_c))) %>% #find difference between two subsequent temp measurements to identify anomalies
  mutate(diff2 = temp_c - lead(temp_c, default = first(temp_c))) #find difference between two subsequent temp measurements to identify anomalies
  #filter(diff < 1 | diff > -1 | diff2 < 1 | diff2 > -1)

#Thank you stas g (https://stats.stackexchange.com/a/164830) for this function
find_peaks <- function (x, m = 3){
    shape <- diff(sign(diff(x, na.pad = FALSE)))
    pks <- sapply(which(shape < 0), FUN = function(i){
       z <- i - m + 1
       z <- ifelse(z > 0, z, 1)
       w <- i + m + 1
       w <- ifelse(w < length(x), w, length(x))
       if(all(x[c(z : i, (i + 2) : w)] <= x[i + 1])) return(i + 1) else return(numeric(0))
    })
     pks <- unlist(pks)
     pks
}

pk <- find_peaks(lol_check$temp_c, m = 50) #set a high threshold bc we need it...

lol_check_peak <- lol_detide %>%
  filter(date_time < ymd_hms("2021-06-30 23:00:00")) %>%
  arrange(date_time) %>% #make sure observations are in order by date/time
  mutate(diff = temp_c - lag(temp_c, default = first(temp_c))) %>% #find difference between two subsequent temp measurements to identify anomalies
  mutate(diff2 = temp_c - lead(temp_c, default = first(temp_c))) %>% #find difference between two subsequent temp measurements to identify anomalies
  #filter(diff < 1 | diff > -1 | diff2 < 1 | diff2 > -1)
  mutate(peak = ifelse(row_number() %in% c(pk) == TRUE, 1, 0)) #if a row ID matches the row ID found by find_peaks, then call it "1" (if not, "0")

ggplot(lol_check_peak, aes(x=date_time)) +
  geom_line(aes(y=tide), color="red") + 
  geom_line(aes(y=temp_c), color="blue") +
  #geom_line(aes(y=diff), color="green") +
  #geom_line(aes(y=diff2), color="orange") +
  geom_point(aes(y=temp_c, color=ifelse(peak>0, "red", "black"), size=ifelse(peak>0, 2, 0))) + #if it's a peak, color it red and make it big
  scale_x_datetime(breaks = scales::date_breaks("1 week"), 
                    labels = date_format("%m/%d %H:%m")) +
  xlab("Date time") +
  theme_bw() +
  theme(axis.text.x=element_text(angle=90)) +
  scale_color_identity() +
  scale_size_identity() +
  geom_vline(aes(xintercept = date_time), lol_check_peak %>% filter(peak == 1)) + #if it's a peak, draw a vertical line
  scale_y_continuous(breaks = round(seq(min(lol_check_peak$tide), max(lol_check_peak$tide), by = 0.5),1))

Looks like anything below a 1.5 tide height is definitely disconnected from the ocean

So now, let’s filter based on anomalous pH measurements

lol_detide_temp_ph <- lol_all %>%
  filter(tide>1.5) %>%
  arrange(date_time) %>% #make sure observations are in order by date/time
  mutate(diff3 = p_h - lag(p_h, default = first(p_h))) #doesn't look like pH values jump extraordinarily

ggplot(lol_detide_temp_ph, aes(x=date_time)) +
  geom_line(aes(y=p_h), color="blue") +
  geom_line(aes(y=diff3), color="green") +
  scale_x_datetime(breaks = scales::date_breaks("1 week"), 
                    labels = date_format("%m/%d %H:%m")) +
  xlab("Date time") +
  theme_bw() +
  theme(axis.text.x=element_text(angle=90))

#And plot it up 
ggplot(lol_detide_temp_ph, aes(x=date_time)) +
  geom_line(aes(y=p_h), color="#009E73") + 
  geom_line(aes(y=temp_c), color="#D55E00") + # Divide by 10 to get the same range than the temperature
  geom_line(aes(y=tide), color="#0072B2") + # Divide by 10 to get the same range than the temperature
  scale_x_datetime(breaks = scales::date_breaks("1 week"), 
                    labels = date_format("%m/%d %H:%m")) +
  annotate(geom="text", x=as.POSIXct("2021-09-24 00:01:00"), y=19, hjust=0, label="Temp (C)", color="#D55E00", size=5) +
  annotate(geom="text", x=as.POSIXct("2021-6-14 00:01:00"), y=9, hjust=-0.1, label="pH", color="#009E73", size=5) +
  annotate(geom="text", x=as.POSIXct("2021-6-14 00:01:00"), y=6, hjust=0, label="Tide", color="#0072B2", size=5) +
  xlab("Date & time") +
  ylab("Value") +
  theme_bw() +
  theme(axis.text.x=element_text(angle=45, vjust = 1, hjust=1, size=12),
        axis.title.x=element_text(size=15),
        axis.text.y=element_text(size=12),
        axis.title.y=element_text(size=15))

ggsave(here("figures", "sensors", "lol_detide_tide-temp.png"), height=20, width=40, units="cm")

Plot up these data using a format similar to Li’s for the LTER data - welcome, highchart!

y1 <- lol_detide_temp_ph$p_h
y2 <- lol_detide_temp_ph$temp_c
y3 <- lol_detide_temp_ph$tide
x <- lol_detide_temp_ph$date_time

plot <- highchart() %>% 
  hc_add_series(data = y1, dashStyle="solid") %>% 
  hc_add_series(data = y2, yAxis = 1) %>% 
  hc_add_series(data = y3, yAxis = 2) %>%
  hc_yAxis_multiples(
     list(lineWidth = 3, lineColor='#D55E00', title=list(text="pH")),
     list(lineWidth = 3, lineColor="#009E73", title=list(text="Temperature")),
     list(lineWidth = 3, lineColor="#0072B2", title=list(text="Tide cycle"))) %>%
    hc_xAxis(title = "Date", categories = x, breaks=10) %>%
  hc_colors(c("#D55E00","#009E73", "#0072B2"))

htmlwidgets::saveWidget(widget = plot, file = here("figures", "sensors", "LOL_highchart.html"))
webshot::webshot(url = here("figures", "sensors", "LOL_highchart.html"), file = here("figures", "sensors", "LOL_highchart.png"), delay=3)

Well that was a bit of a waste of time

Use wave data to check tide vs temperature stuff

#look at July NOAA NDBC data from Santa Maria buoy 
lol_wave <- read_csv(here("data","sensors", "wave-july2021.csv")) %>% 
  clean_names() %>%
  filter(wvht<99.00) %>%
  mutate(mn=mn+5) %>%
  unite("date", "year", "month", "day", sep="/") %>%
  unite("time", "hr", "mn", sep=":") %>%
  unite("date_time", "date", "time", sep=" ") %>%
  mutate(date_time=ymd_hm(date_time)) #apply lubridate to date and time

## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   Year = col_double(),
##   Month = col_double(),
##   Day = col_double(),
##   hr = col_double(),
##   mn = col_double(),
##   WSPD = col_double(),
##   WVHT = col_double(),
##   WTMP = col_double()
## )

#join this with the july sensor df
lol_july <- lol_all %>%
  filter(date_time < ymd_hms("2021-08-01 00:00:00"),
         date_time > ymd_hms("2021-07-01 00:00:00"))

lol_wave_join <- full_join(lol_july, lol_wave) %>%
  fill(wvht, .direction = "updown") %>% #fill in empty wave measurements with previous values before
  distinct(date_time, .keep_all = TRUE) %>% #remove duplicated data (not sure where those came from)
  filter(date_time > ymd_hms("2021-07-20 00:00:00"),
         date_time < ymd_hms("2021-07-25 00:00:00"))

## Joining, by = "date_time"

y1 <- lol_wave_join$temp_c
y2 <- lol_wave_join$wvht
y3 <- lol_wave_join$tide
x <- lol_wave_join$date_time

highchart() %>% 
  hc_add_series(data = y1, dashStyle="solid") %>% 
  hc_add_series(data = y2, yAxis = 1) %>% 
  hc_add_series(data = y3, yAxis = 1) %>%
  hc_yAxis_multiples(
     list(lineWidth = 3, lineColor='#D55E00', title=list(text="Temp")),
     list(lineWidth = 3, lineColor="#009E73", title=list(text="Wave Height")),
     list(lineWidth = 3, lineColor="#0072B2", title=list(text="Tide cycle"))) %>%
    hc_xAxis(title = "Date", categories = x, breaks=10) %>%
  hc_colors(c("#D55E00",
              "#009E73",
              "#0072B2"))

#List of tide height candidates (at these heights, temperature started dropping again):
#2.321 
#2.128 
#2.120
#1.987 
#2.277
#2.587
#2.529
#2.291
#2.315
#2.163
#2.54
#2.72
#2.47
#2.57
#2.43
#2.56
#2.46
#2.68
#2.68

The wave data isn’t a magic bullet, but it does look like I might be overprocessing the data if I make the tide height as high as 3.0. Based on the July data (especially keeping in mind temperature changes will be most apparent in the middle of the day when the sun is out in full force), I’m going to remove any data below a tide height of 2.0

Let’s move on to continuing to process the data for “out of water”

lol_superfilter <- lol_all %>%
  #filter(tide>2.3) %>%
  filter(date_time > ymd_hms("2021-07-01 01:00:00"),
         date_time < ymd_hms("2021-07-10 23:00:00"))

#3.232 looks good, 3.29 is also a solid candidate for tide filtering

#2.3, 2.68 are also good candidates - I want to make sure I'm not over-filtering

y1 <- lol_superfilter$temp_c
y2 <- lol_superfilter$temp_c
y3 <- lol_superfilter$tide
x <- lol_superfilter$date_time

highchart() %>% 
  hc_add_series(data = y2, dashStyle="solid") %>% 
  #hc_add_series(data = y2, yAxis = 1) %>% 
  hc_add_series(data = y3, yAxis = 1) %>%
  hc_yAxis_multiples(
     list(lineWidth = 3, lineColor='#D55E00', title=list(text="pH")),
     list(lineWidth = 3, lineColor="#009E73", title=list(text="Temp")),
     list(lineWidth = 3, lineColor="#0072B2", title=list(text="Tide cycle"))) %>%
    hc_xAxis(title = "Date", categories = x, breaks=10) %>%
  hc_colors(c("#D55E00",
              "#009E73",
              "#0072B2"))

Nevermind it wasn’t a waste of time! Highcharts are super useful! Looks like the pool is definitely disconnected from the ocean at a tide lower than a +3.0…maybe a +3.02 (but possibly even +3.2 or +3.4)… but to be safe, let’s stick with +2.0, I don’t want to overfilter the data.

Create df for LOL with values above tides of +3.232, and remove (or smooth?) anomalous time periods

lol_detided <- lol_detide_temp_ph %>%
  filter(tide>3.232) %>%
  filter(!(date_time >= ymd_hms("2021-07-31 13:30:00") & date_time <= ymd_hms("2021-07-31 14:00:00")),
         (date_time != ymd_hms("2021-09-15 09:15:00")),
         !(date_time >= ymd_hms("2021-09-16 01:30:00") & date_time <= ymd_hms("2021-09-16 02:45:00")), #anomalous points here identified by comparing durafet and hobo temp logger values
         !(date_time >= ymd_hms("2021-08-01 13:45:00") & date_time < ymd_hms("2021-08-01 15:00:00"))) %>%
  drop_na() #remove times with tides but without pH values

ALG

Scrape tide data from http://tbone.biol.sc.edu/tide/

alg_tide <- read_html("http://tide.arthroinfo.org/tideshow.cgi?tplotdir=horiz;gx=640;gy=240;caltype=ndp;type=mrare;interval=00%3A15;glen=98;units=feet;year=2021;month=06;day=12;hour=01;min=00;tzone=local;d_year=;d_month=01;d_day=01;d_hour=00;d_min=00;ampm24=24;site=Gaviota%2C%20California") %>%
  html_elements("pre") %>% #select only the date, time, and tide values from the webpage
  html_text2() %>% #convert list to data table
  data.frame() %>% #convert table to data frame
  mutate(date_tide = str_split(., pattern = "\n")) %>% #split into rows by each time point
  unnest(date_tide) %>% #unnest into two columns 
  mutate(date_tide=as.factor(date_tide)) %>% #make column values factors 
  separate(date_tide, into = c("date", "space", "time", "time_zone", "tide"), sep="\\s") %>% #separate the values (separated by spaces) into their own columns
  select(-"space", -".") %>% #remove the "space" (blank space) column and duplicated column created by unnest() 
  unite("time", "time", "time_zone", sep="\ ",) %>% #join together time and time zone
  unite("date_time", "date", "time", sep="\ ") %>% #join together date and time/time zone
  drop_na() %>% #remove final row with NA (not sure why that's even there)
  mutate(date_time=ymd_hm(date_time), #apply lubridate to date/time column
         tide=as.numeric(tide)) #coerce tide values from character to numeric

Plot it up

ggplot(alg_tide, aes(x=date_time, y=tide)) +
  geom_line(size=0.7) +
  scale_x_datetime(breaks = scales::date_breaks("2 days"), 
                    labels = date_format("%m/%d %H:%m")) +
  xlab("Date time") +
  ylab("Tide height") +
  theme_bw() +
  theme(axis.text.x=element_text(angle=90))

ggsave(here("figures", "sensors", "alg_tide.png"), height=20, width=40, units="cm")

Combine the tides with the pH/temp values for ALG, plot it up!

# filter all data for ALG
alg <- all %>%
  filter(site=="Alegria")

alg_all <- full_join(alg, alg_tide)

## Joining, by = "date_time"

alg_plot <- alg_all %>%
  pivot_longer(cols=temp_c:tide,
               names_to = "data",
               values_to = "value")
  
ggplot(alg_plot, aes(x=date_time, y=value, group=data)) +
  geom_line(aes(color=data), size=0.7) +
  scale_x_datetime(breaks = scales::date_breaks("1 week"), 
                    labels = date_format("%m/%d %H:%m")) +
  xlab("Date time") +
  theme_bw() +
  theme(axis.text.x=element_text(angle=90))

ggsave(here("figures", "sensors", "alg_pH_temp_tide.png"), height=20, width=40, units="cm")

Remove anomalous measurements (like where the sensor was likely inundated with sand!)

alg_desand <- alg_all %>%
  filter(date_time > ymd_hms("2021-06-18 01:00:00")) %>% #super weird, but pH looks like it stabilizes at 1AM post sand
  drop_na() %>% #remove rows with only tide values
  distinct(date_time, .keep_all = TRUE) #remove duplicated data (not sure where those came from)

ggplot(alg_desand, aes(x=date_time, y=p_h)) +
  geom_line(size=0.7) +
  scale_x_datetime(breaks = scales::date_breaks("1 week"), 
                    labels = date_format("%m/%d %H:%m")) +
  xlab("Date time") +
  theme_bw() +
  theme(axis.text.x=element_text(angle=90))

Check anomalous points and filter by tide height given pH patterns

alg_highchart <- alg_desand %>%
  filter(date_time >= ymd_hms("2021-07-17 01:00:00") & date_time <= ymd_hms("2021-07-27 01:00:00")) %>%
  filter(tide>0.9)

y1 <- alg_highchart$p_h
y2 <- alg_highchart$temp_c
y3 <- alg_highchart$tide
x <- alg_highchart$date_time

highchart() %>% 
  hc_add_series(data = y1, dashStyle="solid") %>% 
  hc_add_series(data = y3, yAxis = 1) %>% 
  #hc_add_series(data = y3, yAxis = 2) %>%
  hc_yAxis_multiples(
     list(lineWidth = 3, lineColor='#D55E00', title=list(text="pH")),
     #list(lineWidth = 3, lineColor="#009E73", title=list(text="Temp")),
     list(lineWidth = 3, lineColor="#0072B2", title=list(text="Tide cycle"))) %>%
    hc_xAxis(title = "Date", categories = x) %>%
  hc_colors(c("#D55E00",
              "#009E73", 
              "#0072B2"))

These points look fine (and filtering should be easy since the sensor is always connected to the ocean) - but the high chart is suggesting a pattern around 0.00 tide (so let’s try removing everything below 0.00… actually, let’s try 0.6 since some of those pH values are quite low and consistently dropping at low tide it seems)

Tried 0.00, but still seeing steep drop in pH with tide cycle Tried 0.60, but still seeing steep drop in pH Tried 0.80, but still seeing some drop in pH 0.90 appears to be OK at not creating massive drop in pH but still having some reasonable cycle

Detide ALG data and plot

alg_detide <- alg_desand %>%
  filter(tide>0.9)

ggplot(alg_detide, aes(x=date_time)) +
  geom_line(aes(y=p_h), color="#009E73") + 
  geom_line(aes(y=temp_c), color="#D55E00") + # Divide by 10 to get the same range than the temperature
  geom_line(aes(y=tide), color="#0072B2") + # Divide by 10 to get the same range than the temperature
  scale_x_datetime(breaks = scales::date_breaks("4 days"), 
                    labels = date_format("%m/%d %H:%m")) +
  annotate(geom="text", x=as.POSIXct("2021-6-18 00:01:00"), y=19, hjust=0, label="Temp (C)", color="#D55E00", size=5) +
  annotate(geom="text", x=as.POSIXct("2021-6-18 00:01:00"), y=10, hjust=-0.1, label="pH", color="#009E73", size=5) +
  annotate(geom="text", x=as.POSIXct("2021-6-18 00:01:00"), y=-1.5, hjust=0, label="Tide", color="#0072B2", size=5) +
  xlab("Date & time") +
  ylab("Value") +
  theme_bw() +
  theme(axis.text.x=element_text(angle=45, vjust = 1, hjust=1, size=12),
        axis.title.x=element_text(size=15),
        axis.text.y=element_text(size=12),
        axis.title.y=element_text(size=15))

ggsave(here("figures", "sensors", "alg_detide.png"), height=20, width=40, units="cm")

There’s still some noise at a few dates, let’s filter those dates out of the data ### Remove (or smooth?) time periods with a LOT of noise

alg_no_noise <- alg_detide %>%
  filter(!(date_time > ymd_hms("2021-07-17 11:00:00") & date_time < ymd_hms("2021-07-17 19:30:00"))) %>% #this is a chunk of time with a lot of noise
  filter(!(date_time %in% (ymd_hms("2021-06-19 10:15:00", #remove time points with significant jumps in the wrong direction
                                  "2021-06-29 14:15:00",
                                  "2021-06-29 15:30:00",
                                  "2021-06-30 11:45:00",
                                  "2021-06-30 14:15:00",
                                  "2021-06-30 16:15:00",
                                  "2021-06-30 19:15:00",
                                  "2021-07-02 06:15:00",
                                  "2021-07-02 07:45:00",
                                  "2021-07-02 10:45:00",
                                  "2021-07-04 09:45:00",
                                  "2021-07-04 12:00:00",
                                  "2021-07-04 15:15:00"))))

y1 <- alg_no_noise$p_h
y3 <- alg_no_noise$tide
x <- alg_no_noise$date_time

highchart() %>% 
  hc_add_series(data = y1, dashStyle="solid") %>% 
  hc_add_series(data = y3, yAxis = 1) %>% 
  hc_yAxis_multiples(
     list(lineWidth = 3, lineColor='#D55E00', title=list(text="pH")),
     list(lineWidth = 3, lineColor="#0072B2", title=list(text="Tide cycle"))) %>%
    hc_xAxis(title = "Date", categories = x) %>%
  hc_colors(c("#D55E00", 
              "#0072B2"))

Now let’s actually plot up a comparison of the pH values across sites

#join all sites into one df (and remove diff columns)
ph_all <- merge_recurse(list(alg_no_noise, lol_detided, bml_detide_temp)) %>%
  select(-diff3)

#set order for sites
ph_all$site <- factor(ph_all$site, levels=c("Lompoc Landing","Alegria","Bodega Bay"))

#set custom color for sites
pal <- c(
  "Alegria" = "#D55E00",
  "Lompoc Landing" = "#009E73", 
  "Bodega Bay" = "#0072B2"
)

ggplot(ph_all, aes(x=date_time, y=p_h, group=site)) +
  geom_line(aes(color=site), size=0.7, alpha=0.8) +
  scale_color_manual(values = pal) + #color lines by custom site color palette
  scale_x_datetime(breaks = scales::date_breaks("1 week"), 
                    labels = date_format("%m/%d %H:%m")) +
  xlab("Date time") +
  ylab("pH") +
  theme_bw() +
  theme(axis.text.x=element_text(angle=45, vjust = 1, hjust=1, size=12),
        axis.title.x=element_text(size=15),
        axis.text.y=element_text(size=12),
        axis.title.y=element_text(size=15),
        legend.position = "none")

ggsave(here("figures", "sensors", "ph_all.png"), height=20, width=40, units="cm")

# For presentations, highlight one line at a time per site
#BML focal
ph_all$site <- factor(ph_all$site, levels=c("Lompoc Landing","Alegria","Bodega Bay"))

ggplot(ph_all, aes(x=date_time, y=p_h, group=site)) +
  geom_line(aes(color=site, alpha=site), size=0.7) +
  scale_color_manual(values = pal) + #color lines by custom site color palette
  scale_x_datetime(breaks = scales::date_breaks("1 week"), 
                    labels = date_format("%m/%d %H:%m")) +
  xlab("Date time") +
  ylab("pH") +
  theme_bw() +
  theme(axis.text.x=element_text(angle=45, vjust = 1, hjust=1, size=12),
        axis.title.x=element_text(size=15),
        axis.text.y=element_text(size=12),
        axis.title.y=element_text(size=15),
        legend.position = "none") +
  scale_alpha_manual(values=c(0.5,0.5,1))

ggsave(here("figures", "sensors", "ph_bml.png"), height=20, width=40, units="cm")

#LOL focal
ph_all$site <- factor(ph_all$site, levels=c("Alegria","Bodega Bay", "Lompoc Landing"))

ggplot(ph_all, aes(x=date_time, y=p_h, group=site)) +
  geom_line(aes(color=site, alpha=site), size=0.7) +
  scale_color_manual(values = pal) + #color lines by custom site color palette
  scale_x_datetime(breaks = scales::date_breaks("1 week"), 
                    labels = date_format("%m/%d %H:%m")) +
  xlab("Date time") +
  ylab("pH") +
  theme_bw() +
  theme(axis.text.x=element_text(angle=45, vjust = 1, hjust=1, size=12),
        axis.title.x=element_text(size=15),
        axis.text.y=element_text(size=12),
        axis.title.y=element_text(size=15),
        legend.position = "none") +
  scale_alpha_manual(values=c(0.5,0.5,1))

ggsave(here("figures", "sensors", "ph_lol.png"), height=20, width=40, units="cm")

#ALG focal
ph_all$site <- factor(ph_all$site, levels=c("Lompoc Landing","Bodega Bay", "Alegria"))

ggplot(ph_all, aes(x=date_time, y=p_h, group=site)) +
  geom_line(aes(color=site, alpha=site), size=0.7) +
  scale_color_manual(values = pal) + #color lines by custom site color palette
  scale_x_datetime(breaks = scales::date_breaks("1 week"), 
                    labels = date_format("%m/%d %H:%m")) +
  xlab("Date time") +
  ylab("pH") +
  theme_bw() +
  theme(axis.text.x=element_text(angle=45, vjust = 1, hjust=1, size=12),
        axis.title.x=element_text(size=15),
        axis.text.y=element_text(size=12),
        axis.title.y=element_text(size=15),
        legend.position = "none") +
  scale_alpha_manual(values=c(0.5,0.5,1))

ggsave(here("figures", "sensors", "ph_alg.png"), height=20, width=40, units="cm")

# Save the "final" df to a .csv file
write.csv(ph_all, here("data", "sensors", "ph_clean.csv"), row.names = FALSE)

Now, for some “Chan et al 2017” style analyses

Look at the frequency of pH values below 7.8

ph_7.8 <- ph_all %>%
  mutate(threshold = ifelse(p_h<7.8, "below", "above")) %>%
  group_by(site, threshold) %>%
  summarize(tot_observations = n()) %>%
  ungroup() %>%
  add_row(site="Alegria", threshold="below", tot_observations=0) #add this row because Alegria has 0 observations below 7.8 (for now...)

## `summarise()` has grouped output by 'site'. You can override using the `.groups` argument.

#Get total number of observations - use double square brackets because it's a tibble
lol_n <- ph_7.8[[1,3]] + ph_7.8[[2,3]] 
bml_n <- ph_7.8[[3,3]] + ph_7.8[[4,3]]
alg_n <- ph_7.8[[5,3]] + ph_7.8[[6,3]]

#Get frequency of pH values below 7.8
lol_7.8 <- ph_7.8[[2,3]]/lol_n
bml_7.8 <- ph_7.8[[4,3]]/bml_n
alg_7.8 <- ph_7.8[[6,3]]/alg_n

Create a nice table

# Calculate min, max, average pH values for each site
alg_min <- (min(alg_no_noise$p_h))
alg_max <- (max(alg_no_noise$p_h))
alg_median <- (median(alg_no_noise$p_h))
alg_mean <- (mean(alg_no_noise$p_h))
alg_sd <- (sd(alg_no_noise$p_h))
alg_cv <- (alg_sd/alg_mean)*100 #coefficient of variation

lol_min <- (min(lol_detided$p_h))
lol_max <- (max(lol_detided$p_h))
lol_median <- (median(lol_detided$p_h))
lol_mean <- (mean(lol_detided$p_h))
lol_sd <- (sd(lol_detided$p_h))
lol_cv <- (lol_sd/lol_mean)*100 #coefficient of variation

bml_min <- (min(bml_detide_temp$p_h))
bml_max <- (max(bml_detide_temp$p_h))
bml_median <- (median(bml_detide_temp$p_h))
bml_mean <- (mean(bml_detide_temp$p_h))
bml_sd <- (sd(bml_detide_temp$p_h))
bml_cv <- (bml_sd/bml_mean)*100 #coefficient of variation

ph_table <- tribble(
  ~"Site", ~"Min pH", ~"Max pH", ~"Median pH",  ~"CV", 
  "Bodega Marine Lab", bml_min, bml_max, bml_median, bml_cv,
  "Lompoc Landing", lol_min, lol_max, lol_median, lol_cv,
  "Alegria", alg_min, alg_max, alg_median, alg_cv) 

ph_table %>%
  gt() %>%
  fmt_number(
  columns = c("Min pH":"Median pH"),
  rows = everything(),
  decimals = 2) %>%
  data_color(
    columns = c("Site"),
    colors = scales::col_factor( # <- bc it's a factor
      palette = c("#D55E00","#0072B2","#009E73"),
      domain = c("Bodega Marine Lab", "Lompoc Landing", "Alegria"))) %>%
  gtsave(here("figures", "sensors", "ph_table.png"))

Summary (what I did)

At BML, sampling interval started at every 10 minutes and then switched to 15 minutes for all sites

Filtering for tide heights based on temp/pH patterns - All points at ALG were removed at tide heights below 0.9 - All points at LOL were removed at tide heights below 2.0 (3.232 was also a good option, but I don’t want to remove more values than need be… ask about this) - All points at BML were removed at tide heights below -0.5

Removed additional anomalous time points (noise or major jumps, especially in wrong direction) - 20 from BML - All data collected before 06/18/2021 at ALG, plus 46 extra points - 9 from LOL

Zoom in on LOL as an example of pH variation within tidepool

Create df (remove anomalous points)

lol_cycle <- lol_all %>%
    drop_na() %>% #remove times with tides but without pH values
  filter(!(date_time >= ymd_hms("2021-07-31 13:30:00") & date_time <= ymd_hms("2021-07-31 14:00:00")), #remove anomalous points (major jumps in pH)
         !(date_time >= ymd_hms("2021-08-01 13:45:00") & date_time <= ymd_hms("2021-08-01 15:00:00")),
         !(date_time >= ymd_hms("2021-06-26 16:45:00") & date_time <= ymd_hms("2021-06-26 18:00:00")),
         !(date_time == ymd_hms("2021-07-13 08:00:00")),
         !(date_time == ymd_hms("2021-08-19 03:30:00")),
         !(date_time == ymd_hms("2021-09-15 09:15:00")),
         !(date_time == ymd_hms("2021-08-20 00:15:00"))) %>%
  mutate(cycle=ifelse(tide>=3.232, "high", "low"))

Plot up all data from LOL to show extent of pH and temperature drops within pool - code from Li (thank you)

figure <- function(df_input,name) {
  
m <- list(
  l = 10,
  r = 10,
  b = 30,
  t = 30,
  pad = 4
)

fig <- plot_ly(width = 2000)
 
 fig <- fig %>% 
   add_trace(data = df_input, y=~p_h, x = ~date_time, name = "pH", yaxis = "y1", mode = "lines", type = "scatter", line=list(color="green")) 

y2 <- list(
  tickfont = list(color = "red"),
  titlefont = list(color = "red"),
  overlaying = "y",
  side = "left",
  anchor="free",
  position=0.02,
  title = "Temperature (\u00B0C)")

fig <- fig %>% 
  add_trace(data =df_input, y=~temp_c, x = ~date_time, name = "Temperature (\u00B0C)", yaxis = "y2", mode = "lines", type = "scatter", line=list(color="red"))

fig <- fig %>% 
  layout(
    title = name, yaxis2 = y2, #yaxis3 = y3,
    xaxis = list(title = 'Date',
                 domain = c(0.06, 1),
                 tick0 = "2021-06-14 08:00:00",
                 dtick = 7*86400000.0, 
                 tickformat="%d-%b"),
    yaxis = list(title = "pH",
              tickfont = list(color = "green"),
              titlefont = list(color = "green"),
              side="left",
              anchor="free",
              position=0.05),
  showlegend = FALSE,
  margin = m
)

return(fig)

}

#save it


lol_cycle_fig <- figure(lol_cycle,"Lompoc Landing")

orca(lol_cycle_fig, file="lol-cycle-full.png") #save plotly image
filesstrings::move_files("lol-cycle-full.png", here("figures", "sensors"), overwrite=TRUE) #move the image to the "Figures" folder

## 1 file moved. 0 failed.

Alternatively, check out https://plotly.com/r/multiple-axes/

Next steps: 1. assess the greatest pH and temperature range in a 24 hour, 1 week period and monthly averages for LOL

2. Re-filter all pH data based on HOBO temp data (especially, make annotations next to every single point/range that was filtered out, for future QC)

3. Talk to someone about overfiltering vs underfiltering LOL data - look at code chunk 595 (3.232 tide) vs 530 (2.0 tide)